Correlation vs. Causation

Correlation is a statistical measure that describes the extent to which two variables change together. It can be positive, negative, or zero.

A strong positive correlation indicates that as one variable increases, the other variable also tends to increase.

A strong negative correlation indicates that as one variable increases, the other variable tends to decrease.

Correlation coefficients range from -1 to +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation.

If two variables have no correlation, it means that there is no predictable relationship between them, and changes in one variable do not affect the other.

The r-value is interpreted as follows: 0.0-0.1 (no correlation), 0.1-0.3 (weak correlation), 0.3-0.5 (moderate correlation), 0.5-0.7 (strong correlation), 0.7-1.0 (very strong correlation).

Correlation indicates a relationship between two variables, while causation implies that one variable directly affects the other.

An example of a strong positive correlation is the relationship between height and weight in children; as height increases, weight tends to increase.

An example of a strong negative correlation is the relationship between the amount of exercise and body weight; as exercise increases, body weight tends to decrease.

An r-value of -0.89 indicates a strong negative correlation between the two variables.